Forum: Your data already knows what you don’t
The marriage of two “natural resources” – hydrocarbons and data – will transform unconventional oil development.
By Atanu Basu (right), Daniel Mohan and Marc Marshall
Known-knowns, known-unknowns and unknown-unknowns. Donald Rumsfeld’s notable turn of phrase is an apt characterization of where we are with unconventional oil development today. Shale operators in Eagle Ford (South Texas), Permian (West Texas), Bakken (Upper Midwest) and other places have transformed the United States into an energy superpower by profitably extracting oil and gas from tight rocks that weren’t commercially viable even a few years ago. With that backdrop, unconventional oil development today is punctuated by significant performance variations among operators with contiguous acreage positions and meager estimated ultimate recovery (EUR) rates. Unless performance keeps improving, any fluctuation in commodity prices can send shockwaves through the oil patches around the country, as we have seen happen with natural gas. How do we gain ground on the vexing “unknowns” to tilt the inherent risks involved in shale oil development in our favor?
Standing on the shoulder of giants
Geoscience (geoscientists are the giants of the energy industry) is finally getting a shot in the arm from data science, especially from Google-like technologies that are already at work in the oil patch. Leading the charge is prescriptive analytics, which can “prescribe” optimum recipes for drilling, completing and producing wells to maximize an asset’s value at every point during its operational lifetime. The premise of prescriptive analytics is to take in all data – Figure 1 shows examples of shale data sets – and use the data to predict and prescribe how to make better wells using information from the past wells and subsurface characteristics of undrilled acreage.
Figure 1: Examples of shale data sets.
While today’s sophisticated operators and energy services companies are adept at analyzing each of these data sets separately, prescriptive analytics technology is unique in that it processes these structured and unstructured data sets together, and does so continually. Since reservoir conditions are anything but static, the machine learns from new streams of data and updates its “prescriptions” when the data sets signal the need for a recalibration. This adaptive environment compresses learning curves, enabling better decisions faster, with less risk – and much less capital.
Questions worth answering
Let’s “begin with the end in mind” – as the late Dr. Steven Covey used to say – and understand the outcomes made possible by prescriptive analytics, using data sets most operators already have on hand and/or routinely collect in the course of normal operations. Some examples:
- Which reservoir, drilling, completion and production variables have the greatest impact on production?
- How closely should we space wells? Do we have stage overlap? Formation containment?
- Does the order in which we treat and/or produce adjacent wells matter? Why?
- Which stages and clusters were treated effectively? Treated as expected? Why?
- Which stages are producing? Producing as expected? Which are not? Why?
- How should a well be produced to maximize its lifetime value?
Secondary Recovery, EOR
- When should artificial lift be introduced in the lifecycle of a well to maximize estimated ultimate recovery (EUR)?
- When should enhanced oil recovery (EOR) be introduced in the lifecycle of a well in order to maximize EUR?
- Does EOR result in higher recovery rates, or are recoveries simply accelerated?
- What is the incremental return on investment of EOR? Where is the point of diminishing returns?
You don’t have to know as long as your data does
Operators can start using – and reaping benefits from – data-driven prescriptions immediately, even if the underlying causalities are not fully understood. Think about this for a minute. We use Google to find restaurants or plan a route that avoids traffic congestion without completely understanding how or why the engine’s algorithms produced the suggestions they did. Does that lack of understanding make the results less useful? Of course not.
The same holds true for prescriptive analytics. The technology makes an immediate impact while the geoscientists, in parallel, strive to understand the physics behind the predictions and prescriptions the software derived from an operators’ data – lots and lots of data of all types.
Atanu Basu (email@example.com) is president and CEO of Ayata, a prescriptive analytics software company based in Houston, Texas, and a member of INFORMS. Daniel Mohan is senior vice president of sales & marketing, and Marc Marshall is the senior vice president of engineering SVP at Ayata. A version of this article appeared in Hart Energy’s blog site, E&P. Reprinted with permission.